Our architecture design is based on two important experiences. 1) The patchity stem implemented by a stride-\(p\)  \(p \times p\)  convolution (\(p = 16\)  by default) in the standard ViT is the key reason of training instability [1]}. Recent works show the convolution stem [2]}, [3]} stacked with several convolution, BN and ReLU layers can significantly improve training stability and peak performance. 2) Data bias is a critical challenge for person ReID. By learning invariant representation, IBNNet-a block [4]} has achieved a huge success in many publications or academic challenges [5]}, [6]}.
